The CSV file in this repository contains data from the evaluation process of a taxonomy created by the same author. The taxonomy is a classification model for all elements adversarial to an autonomous vehicle. The initial data from the evaluation process were gathered from the California Department of Motor Vehicles Autonomous Vehicle Collision Reports (https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/autonomous-vehicle-collision-reports/). The California data is collected from manufacturers who tested autonomous vehicles on California roads. Any collision that resulted in property damage, bodily injury, or death should be reported. The California dataset is then used to test the efficacy of the taxonomy in its ability to classify all elements present at the scene of the accidents.

The data used is from January to Sepember of 2023. 117 samples were found, but only 116 samples were used since one sample displayed inconsistencies in the report. The dataset presented here contains both links to original accident reports and the results of the classification in the taxonomy. Fields are described below:

Column 1: Date of the accident.
Column 2: The taxonomy class which was found to be the primary contributing factor in the accident.
Column 3: Any other possibly contributing factors.
Column 4: Complexity level of finding the contributing factors in the taxonomy. Self-assessed levels: 1 for "easy," 2 for "moderate," 3 for "hard," and 4 for an element "not found."
Column 5: Summary of the event.
Column 6: Side of the autonomous vehicle sustaining impact.
Column 7: Driving mode of the autonomous vehicle at the moment of the impact.
Column 8: Link to the original California report.

The academic article and taxonomy associated with this dataset will be posted once published.